IMPACT OF DIABATIC PROCESSES IN AGCM ON 4-DIMENSIONAL VARIATIONAL DATA ASSIMILATION*

The impact of diabatic processes on 4-dimensional variational data assimilation (4D-Var) was studied using the 1995 version of NCEP's global spectral model with and without full physics.The adjoint was coded manually.A cost function measuring spectral errors of 6-hour forecasts to "observation" (the NCEP reanalysis data) was minimized using the L-BFGS (the limited memory quasi-Newton algorithm developed by Broyden,Fletcher,Goldfard and Shanno) for optimizing parameters and initial conditions.Minimization of the cost function constrained by an adiabatic version of the NCEP global model converged to a minimum with a significant amount of decrease in the value of the cost function.Minimization of the cost function using the diabatic model, however,failed after a few iterations due to discontinuities introduced by physical parameterizations.Examination of the convergence of the cost function in different spectral domains reveals that the large-scale flow is adjusted during the first 10 iterations,in which discontinuous diabatic parameterizations play very little role.The adjustment produced by the minimization gradually moves to relatively smaller scales between 10-20th iterations.During this transition period,discontinuities in the cost function produced by "on-off" switches in the physical parameterizations caused the cost function to stay in a shallow local minimum instead of continuously decreasing toward a deeper minimum. Next,a mixed 4D-Var scheme is tested in which large-scale flows are first adiabatically adjusted to a sufficient level,followed by a diabatic adjustment introduced after 10 to 20 iterations. The mixed 4D-Var produced a closer fit of analysis to observations,with 38% and 41% more decrease in the values of the cost function and the norm of gradient,respectively,than the standard diabatic 4D-Var,while the CPU time is reduced by 21%.The resulting optimal initial conditions improve the short-range forecast skills of 48-hour statistics.The detrimental effect of parameterization discontinuities on minimization was also reduced.     

北京大灰厂试验场重力变化特征及其解释

介绍了北京大灰厂试验场（简称京西试验场）的概况，对１９９３年３月以来重力重复观测资料进行了处理。计算了各期重力观测精度，结果显示１９９３年３月～１９９５年５月京西试验场存在一定重力变化。采用给定的模型反演了八宝山断层介质的密度变化，认为断层破碎带介质密度的变化可能是引起地表重力变化的主要因素     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

基于变网格聚类的离群数据的研究

随着计算机技术的迅速发展,计算机在过程控制中起着越来越重要的作用,在集散控制系统中,上位机通过PCU(Process ControlUnit)传送来的数据进行分析处理而后对PCU实现参数整定、报警与管理。因此这一领域的数据挖掘方法研究具有十分重要的意义。文章将根据这一领域的特殊情况,提出一种基于变网格的聚类分析算法分析过程数据库中的离群数据,从而实现系统的预测报警功能。     

地面三维激光雷达点云误差分析及校正方法

分析了地面三维激光雷达点云误差来源,提出了相应的误差校正模型。通过实测数据验证,该模型能够消除系统误差,提高了点云精度。     

基于GIS技术的县域城市规划数据管理模式应用的研究——以广东省增城市为例

随着近年来GIS技术在城乡规划方面的应用日趋普及,GIS技术在空间数据采集、数据管理与分析等关键环节上的优势也逐渐显露。同时,县域城市城乡规划数据种类繁杂,数据量大,亟需先进的技术手段对其进行有效组织与管理。采用GIS技术对城乡规划数据进行动态管理和更新将给城乡规划带来极大的便利。本文以广东省增城市规划管理信息系统为案例,系统阐述GIS技术在城乡规划数据管理中的应用。     

基于马尔科夫链的北斗行驶记录仪数据存储算法研究

介绍了北斗行驶记录仪的组成和功能,对定时1 s和定时60 s报送或存储进行了对比和优劣分析,根据道路匹配特性,给出了一种基于马尔科夫链的北斗行驶记录仪数据存储算法,将马尔科夫链上的节点（拐点）作为记录仪存储或传输的位置点,通过采集车辆状态信息和定位模块输出信息对“拐点”计算方法进行分析和推导,并通过多次实际跑车测试,测试结果表明：与定时1 s报送或存储相比,节省率可达到90％以上,大大节省了存储空间或者通信费用,具有较好的使用效果。     

测震仪器健康状态数据交换平台的设计与实现

介绍了测震仪器健康状态数据交换软件平台的设计与实现.在软件开发过程中,主要使用了TCP/IP Socket编程、并发程序设计和JMS消息机制等关键技术,实现了与多种地震数据采集器的并发式网络通信以及健康状态数据的接收和交换.开源的消息中间件ActiveMQ作为统一交换平台,为仪器健康状态的交换共享提供了统一的数据接口,便于实现系统与新业务的无缝集成,提高了系统的弹性和可扩展性.利用本系统的框架模式开发的测震台站远程监视系统在河北测震台网得到了成功的应用,其功能和性能均达到了设计预期效果.     

东海陆架盆地南部区域地震速度分析

在利用速度谱资料进行速度研究的可行性基础上,使用多种方法对速度谱资料进行甄别、筛选,对有效的速度谱资料进行统计、分析,计算出瓯江、闽江、基隆、晋江4个区域及全区的平均速度趋势,阐明了制作各反射层位平均速度分布图的方法,为本区时深转换提供基础资料。同时,分析了地震地质构造与速度趋势的相关性和一致性,对盆地内构造活动不强的区域等速度线与地层分布趋势及密度的关系进行了探讨,指出其对研究重力资料有一定的指导作用。     

几种网络体系结构中数据包的效率比较分析

介绍了一种新型网络体系结构SUNA，并将它的数据包与其它两种层次型网络体系结构中的数据包进行了组成和效率上的比较分析。说明了在这方面，SUNA的数据处理效率的优势。